Collaborative Research: Extreme OpenMP: A Programming Model for Productive High End Computing

协作研究：Extreme OpenMP：高效高端计算的编程模型

• 批准号: 0833201
• 负责人: Barbara Chapman
• 金额: $ 62.9万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0833201&HistoricalAwards=false
• 关键词: Collaborative; Research; Extreme; OpenMP; Programming

High-end distributed and distributed shared memory platforms with millions of processors will be deployed in the near future to solve the toughest technical problems. Their individual nodes will be heterogeneous multithreading, multicore systems, capable of executing many threads of control, but with relatively little memory per thread, low bandwidth to main memory and deep memory hierarchies.A programming model that supports productive, portable, efficient parallel programming both within and across the nodes of these petascale systems is essential if their potential is to be realized. Since it is easier for application developers and tool vendors to extend existing software rather than adopt a new programming language, a programming model based upon a familiar paradigm is highly desirable.OpenMP is a widely supported shared memory programming model that provides ease of maintenance. It is suitable for programming multicore nodes, but does not address the needs of distributed memory platforms. This research will significantly extend OpenMP so that it can be used to program all levels of a high-end petascale system. In order to accomplish this, the investigators will enhance its existing mechanisms for describing multiple levels of parallelism, provide additional features for specifying synchronization and for achieving high levels of locality, as well as develop a novel I/O interface. Moreover, they will substantially improve the state of the art in OpenMP implementation technology, enabling high performance between nodes as well as within them. Results will be demonstrated via a state-of-the-art Fortran/C/C++ OpenMP compiler, a highly optimized communications library, and a range of large scale applications.

具有数百万处理器的高端分布式和分布式共享内存平台将在不久的将来部署，以解决最棘手的技术问题。它们的各个节点将是异构多线程、多核系统，能够执行许多控制线程，但每个线程的内存相对较少，主内存和深内存层次结构的带宽较低。一种支持高效、可移植、高效并行编程的编程模型如果要实现这些千万亿级系统的潜力，那么它们的节点内部和节点之间就至关重要。由于应用程序开发人员和工具供应商更容易扩展现有软件而不是采用新的编程语言，因此非常需要基于熟悉范例的编程模型。OpenMP 是一种广泛支持的共享内存编程模型，易于维护。它适合对多核节点进行编程，但不能满足分布式内存平台的需求。这项研究将显着扩展 OpenMP，使其可用于对高端千万亿级系统的各个级别进行编程。为了实现这一目标，研究人员将增强其现有的描述多级并行性的机制，提供用于指定同步和实现高水平局部性的附加功能，以及开发一种新颖的 I/O 接口。此外，它们还将大幅提高 OpenMP 实现技术的最新水平，从而实现节点之间以及节点内部的高性能。结果将通过最先进的 Fortran/C/C++ OpenMP 编译器、高度优化的通信库和一系列大型应用程序进行演示。